A hybrid continuum robot design is introduced that combines a proximal tendon-actuated section with a distal telescoping section comprised of permanent-magnet spheres actuated using an external magnet. While, individually, each section can approach a point in its workspace from one or at most several orientations, the two-section combination possesses a dexterous workspace. The paper describes kinematic modeling of the hybrid design and provides a description of the dexterous workspace. We present experimental validation which shows that a simplified kinematic model produces tip position mean and maximum errors of 3% and 7% of total robot length, respectively.

This article introduces the analysis and control of a meso-scale two degree-of-freedom robotic endoscopic tool body for minimally invasive surgeries. The design of the robotic tool uses two types of a tendon-driven joint known as a bending flexure joint that allows us to control each degree-of-freedom by minimizing interjoint coupling by design. Pure kinematic modeling and control for these robots may not provide precise control performance due to kinematic uncertainties arising from tendon elongation, tendon slacking, gear backlash, etc. We propose a static model for each of the joints of the robotic tool that avoids several of these problems. Depending on the direction of tendon tension application, the proximal joint displays considerable hysteresis due to the superelastic material characteristics and this is included in our static model. The statics of a highly compliant distal joint is also modeled and validated using finite element analysis and experimental data. Using these models, we develop a control system that comprises of a disturbance observer and the proposed static model to provide precise force control and compensate for joint hysteresis.

Hydrocephalus in the pediatric population is often treated with endoscopic procedures, in which a rigid endoscope provides a working channel and needed visualization for brain manipulation. The lack of flexible and steerable endoscopic tools limits the ability of the surgeon to conduct complex operations. In this paper, we propose the design of a novel tool for such procedures that uses the compliance of a bending flexural joint to build a compact steerable multi-joint tool. These joints are machined into a tube made of a super-elastic material to create two joints near the tip of the tool. The directional stiffness properties of the flexural joint are exploited to minimize inter-joint coupling. We perform a static analysis of each joint and a kinematic analysis of the entire robot. We follow this with the design of a hand-held controller for this robot, and analyze its ability to control multiple degrees-of-freedom of the robot while minimizing the coupling between joints.

In most cases of peripheral arterial disease (PAD), the operating surgeon must use a variety of catheters riding on a thin wire known as a `guidewire'. This guidewire must be manually navigated through a tortuous pathway of arteries to arrive at the diseased area. Automation of the guidewire therefore reduces surgeon effort and minimizes the time required for a PAD procedure, but is restricted by the size constraints of a standard guidewire. This work presents the design of a robotically actuated 2 degree-of-freedom (DoF) guidewire tip comprised of joints laser micro-machined into a 0.78 mm (<; 2.4 Fr) Nitinol tube. We present an analysis of the notch joint used as a building block in the robot and a control strategy for this type of a joint. The experimental results show that tendon force is an important observable quantity that can be used as a shape sensing mechanism for this type of a joint in practical control applications.

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network's estimated kinematic model to reach a location on a person's body in spite of the person being seated in a bed and covered by a blanket.